Advances in behavioral genetics modeling using Mplus: applications of factor mixture modeling to twin data

Area of Science:

• Psychiatry and Behavioral Genetics
• Quantitative Psychology
• Statistical Genetics

Background:

• Understanding the genetic underpinnings of alcohol-use disorder (AUD) is complex due to population heterogeneity.
• Traditional twin analyses may not fully capture the nuanced genetic architecture within diverse populations.

Purpose of the Study:

• To introduce and illustrate a novel latent variable technique, the factor mixture model, for genetic analyses.
• To apply this model to the analysis of monozygotic-dizygotic twin data for binary alcohol-use disorder items.
• To investigate heritability in relation to both latent class membership and within-class severity dimensions.

Main Methods:

• Development and application of a new factor mixture model.
• Simultaneous analysis of heritability for latent class membership and within-class severity.
• Application to binary twin data measuring alcohol-use disorder.

Main Results:

• The factor mixture model allows for differential heritability across distinct latent classes of individuals.
• Heritability is assessed concerning both group membership and severity within groups.
• The approach demonstrates potential for analyzing genetically heterogeneous populations.

Conclusions:

• The factor mixture model offers a powerful new tool for the genetic analysis of complex traits in heterogeneous populations.
• This technique advances the understanding of genetic influences on alcohol-use disorder by accounting for subgroup-specific heritability.
• Future extensions for longitudinal data are also proposed.